论文标题
最佳生存树
Optimal Survival Trees
论文作者
论文摘要
基于树的模型由于能够识别超出参数模型范围的复杂关系的能力而越来越流行。生存树方法适应了这些模型，以允许对经常出现在医疗数据中的审查结果进行分析。我们提出了一种新的最佳生存树算法，该算法利用了混合企业优化（MIO）和本地搜索技术来生成全球优化的生存树模型。我们证明，OST算法提高了现有生存树方法的准确性，尤其是在大型数据集中。
Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often appear in medical data. We present a new Optimal Survival Trees algorithm that leverages mixed-integer optimization (MIO) and local search techniques to generate globally optimized survival tree models. We demonstrate that the OST algorithm improves on the accuracy of existing survival tree methods, particularly in large datasets.
